Abstract
Nearest neighbor search (NNS) is one of the main concerns in data stream applications since similarity queries can be used in multiple scenarios. Online NNS is usually performed on a sliding window by lazily scanning every element currently stored in the window. This paper proposes Sliding Window-based Incremental Nearest Neighbors (SWINN), a graph-based online search index algorithm for speeding up NNS in potentially never-ending and dynamic data stream tasks. Our proposal broadens the application of online NNS-based solutions, as even moderately large data buffers become impractical to handle when a naive NNS strategy is selected. SWINN enables efficient handling of large data buffers by using an incremental strategy to build and update a search graph supporting any distance metric. Vertices can be added and removed from the search graph. To keep the graph reliable for search queries, lightweight graph maintenance routines are run. According to experimental results, SWINN is significantly faster than performing a naive complete scan of the data buffer while keeping competitive search recall values. We also apply SWINN to online classification and regression tasks and show that our proposal is effective against popular online machine learning algorithms.

Abstract
Lack of timeliness and capacity are seen as fundamental problems that jeopardise healthcare delivery systems everywhere. Many believe the shortage of medical providers is causing this timeliness problem. This action research presents how one doctor implemented the theory of constraints (TOC) to improve the throughput (quantity of patients treated) of his ophthalmology imaging practice by 64% in a few weeks with little to no expense. The five focusing steps (5FS) guided the TOC implementation - which included the drum-buffer-rope scheduling and buffer management - and occurred in a matter of days. The implementation provided significant bottom-line results almost immediately. This article explains each step of the 5FS in general terms followed by specific applications to healthcare services, as well as the detailed use in this action research. Although TOC successfully addressed the practice problems, this implementation was not sustained after the TOC champion left the organisation. However, this drawback provided valuable knowledge. The article provides insightful knowledge to help readers implement TOC in their environments to provide immediate and significant results at little to no expense.

Abstract
PurposeTo develop, implement and assess the results of psychoeducation to improve the QoL of parents with CHD newborns.MethodsParticipants were parents of inpatient newborns with the diagnosis of non-syndromic CHD. We conducted a parallel RCT with an allocation ratio of 1:1 (intervention vs. control), considering the newborns, using mixed methods research. The intervention group received psychoeducation (Parental Psychoeducation in CHD [PPeCHD]) and the usual routines, and the control group received just the regular practices. The allocation concealment was assured. PI was involved in enrolling participants, developing and implementing the intervention, data collection and data analysis. We followed the Consolidated Standards of Reporting Trials (CONSORT) guidelines.ResultsParents of eight newborns were allocated to the intervention group (n = 15 parents) and eight to the control group (n = 13 parents). It was performed as an intention-to-treat (ITT) analysis. In M2 (4 weeks), the intervention group presented better QoL levels in the physical, psychological, and environmental domains of World Health Organization Quality of Life instrument (WHOQOL-Bref). In M3 (16 weeks), scores in physical and psychological domains maintained a statistically significant difference between the groups.ConclusionsThe PPeCHD, the psychoeducational intervention we developed, positively impacted parental QoL. These results support the initial hypothesis. This study is a fundamental milestone in this research field, adding new essential information to the literature.

Abstract
In this paper, a solution to monitor the location of humans during their activity in the agriculture sector with the aim to boost productivity and efficiency is provided. Our solution is based on map-matching methods, that are used to track the path spanned by a worker along a specific activity in an agriculture culture. Two different cultures are taken into consideration in this study — olives and vines. We leverage the symmetry of the geometry of these cultures into our solution and divide the problem three-fold — initially, we estimate a path of a worker along the fields, then we apply the map-matching to such path and finally, a post-processing method is applied to ensure local continuity of the sequence obtained from map-matching. The proposed methods are experimentally evaluated using synthetic and real data in the region of Mirandela, Portugal. Evaluation metrics show that results for synthetic data are robust under several sampling periods, while for real-world data, results for the vine culture are on par with synthetic, and for the olive culture performance is reduced. © 2023 Elsevier B.V.

Abstract
Medical image analysis is crucial for the efficient diagnosis of many diseases. Typically, hospitals maintain vast repositories of images, which can be leveraged for various purposes, including research. However, access to such image collections is largely restricted to safeguard the privacy of the individuals whose images are being stored, as data protection concerns come into play. Recently, the development of solutions for Automated Medical Image Analysis has gained significant attention, with Deep Learning being one solution that has achieved remarkable results in this area. One promising approach for medical image analysis is Federated Learning (FL), which enables the use of a set of physically distributed data repositories, usually known as nodes, satisfying the restriction that the data do not leave the repository. Under these conditions, FL can build high-quality, accurate deep-learning models using a lot of available data wherever it is. Therefore, FL can help researchers and clinicians diagnose diseases and support medical decisions more efficiently and robustly. This article provides a systematic survey of FL in medical image analysis, specifically based on Magnetic Resonance Imaging, Computed Tomography, X-radiography, and histology images. Hence, it discusses applications, contributions, limitations, and challenges and is, therefore, suitable for those who want to understand how FL can contribute to the medical imaging domain.

Abstract
Background: The role of mindful eating (ME) and intuitive eating (IE) in improving eating behavior, diet quality, and health is an area of increasing interest. Objective: The objective of this review was to identify the instruments used to assess ME and IE among higher education students and outcomes related to these dimensions. Methods: This review was carried out according to the PRISMA statement, through systematic searches in PubMed, Web of Science, PsycInfo, and Scopus. The inclusion criteria selected for higher education students, levels of ME and/or IE reported, and observational and clinical studies. The exclusion criteria selected against reviews, qualitative studies, and case studies. Quality was assessed using the Academy of Nutrition and Dietetics Quality Criteria Checklist. Results: A total of 516 initial records were identified, from which 75 were included. Cross-sectional studies were the most common research design (86.7%). Most studies were conducted with samples that were predominantly female (90.7%), White (76.0%), aged 18 to 22 years (88.4%), with BMI < 25 kg/m(2) (83.0%), and in the United States (61.3%). The Intuitive Eating Scale (IES), the Mindful Eating Questionnaire (MEQ), and their different versions were the most used instruments. The outcomes most studies included were eating behavior and disorders (77.3%), anthropometric assessments (47.8%), mental health (42.0%), and body image (40.6%). Regarding the quality of studies, 34.7% of studies were assigned a positive, 1.3% a negative, and 64.0% a neutral rate. Conclusions: IES and MEQ were the most used instruments. RCT and cohort studies are scarce, and future research with a higher level of quality is needed, especially on the topics of food consumption, diet quality, and biochemical markers.